Abstract
Proper identification of environment’s air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max–min operations in “or” and “and” in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM10) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia’s air quality as “good.” The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.
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Abdullah, L. (2007). Procedural knowledge in the presence of a Computer Algebra System (CAS): Rating the drawbacks using a multi-factorial evaluation approach. International Journal for Technology in Mathematics Education, 14(1), 14–20.
Afroz, R., Hassan, M. N., & Ibrahim, N. A. (2003). Review of air pollution and health impacts in Malaysia. Environmental Research, 92(2), 71–77.
Alvarez-Guerra, E., Molina, A., Viguri, J. R., & Alvarez-Guerra, M. (2010). A SOM-based methodology for classifying air quality monitoring stations. Environmental Progress & Sustainable Energy. doi:10.1002/ep.10474.
Athanasiadis, I. N., & Kaburlasos, V. G. (2006). Air quality assessment using fuzzy lattice reasoning. IEEE International Conference on Fuzzy Systems, 29–34. doi:10.1109/FUZZY.2006.1681690.
Awang, M. B., Jaafar, A. B., Abdullah, A. M., Ismail, M. B., Hassan, M. N., Abdullah, R., Johan, S., & Noor, H. (2000). Air quality in Malaysia: impacts, management issues and future challenges. Respirology, 5(2), 183–196.
Bhuyan, S. J., Koelliker, J. K., Marzen, L. J., & Harrington, J. A., Jr. (2003). An integrated approach for water quality assessment of a Kansas watershed. Journal of Environmental Modelling & Software, 18, 473–484.
Chang, N. B., Chen, H. W., & Ning, S. K. (2001). Identification of river water quality using the Fuzzy Synthetic Evaluation approach. Journal of Environmental Management, 63(3), 293–305.
Department of the Environment, Malaysia. (1996). Malaysia Environmental Quality Report. Malaysia: Ministry of Science, Technology and Environment.
Department of the Environment, Malaysia. (2006). Malaysia Environmental Quality Report. Malaysia: Ministry of Science, Technology and Environment.
Fisher, B. (2003). Fuzzy environmental decision-making: applications to air pollution. Journal of Atmospheric Environment, 37, 1865–1877.
Flemming, J., Stern, R., & Yamartino, R. J. (2005). A new air quality regime classification scheme for O3, NO2, SO2 and PM10 observations sites. Atmospheric Environment, 39(33), 6121–6129.
Haiyan, W. (2002). Assessment and prediction of overall environmental quality of Zhuzhou City, Hunan Province, China. Journal of Environmental Management, 66, 329–340.
Icaga, Y. (2007). Fuzzy evaluation of water quality classification. Ecological Indicators, 7(3), 710–718.
Inhaber, H. (1976). Environmental indices. New York: Wiley.
Kantardzic, M. (2003). Data mining: concepts, models, methods and algorithms. New York: John Wiley and Sons.
Lu, R. S., & Lo, S. L. (2002). Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. Water Research, 36, 2265–2274.
Lu, R. S., Lo, S. L., & Hu, J. Y. (1999). Analysis of reservoir water quality using fuzzy synthetic evaluation. Stochastic Environmental Research and Risk Assessment, 13, 327–336.
Onkal-Engin, G., Demir, I., & Hiz, H. (2004). Assessment of air quality in Istanbul using fuzzy synthetic evaluation. Atmospheric Environment, 38, 3804–3815.
Ott, W. R. (1978). Water quality indices: a survey of indices used in the United States. Washington: US Environmental Protection Agency, EPA-600/4-78-005.
Rossbach, M., Jayasekera, R., Kniewald, G., & Thang, N. H. (1999). Large scale air monitoring: lichen vs. air particulate matter analysis. The Science of the Total Environment, 232, 59–66.
Sowlat, H. S., Gharib, H., Yunesan, M., Mahmoudi, M. T., & Lotfi, S. (2011). A novel, fuzzy-based air quality index (FAQI) for air quality assessment. Atmospheric Environment, 45(12), 2050–2059.
Termini, A. G., Abu Osman, M. T., Mahadzirah, M., & Abdullah, L. (2006). Improving the fit between higher learning institutions and employers: fuzzy sets multi-factorial evaluation. Journal of Sustainability Science and Management, 1(2), 55–64.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
Zhang, F., Deng, P., & Xu, P. (2009). Indoor air quality assessment based on grey classification method. Bioinformatics and Biomedical Engineering, ICBBE, 1–4
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Abdullah, L., Khalid, N.D. Classification of air quality using fuzzy synthetic multiplication. Environ Monit Assess 184, 6957–6965 (2012). https://doi.org/10.1007/s10661-011-2472-1
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DOI: https://doi.org/10.1007/s10661-011-2472-1